Preprint Long COVID Risk Loci Implicated from Genome-Wide Association Studies of COVID-19 Susceptibility and Hospitalization, 2025, Cheng

Preprint posted on MedRxiv, now posted with new title on SSRN, see post #4

Integrative Genome-Wide Association Studies of COVID-19 Susceptibility and Hospitalization Reveal Risk Loci for Long COVID

Zhongshan Cheng

Abstract
Long COVID presents a significant public health challenge, characterized by over 200 reported symptoms spanning multiple organ systems. Despite its complexity, genome-wide association studies (GWAS) offer a pathway to uncovering genetic risk factors, though progress has been hindered by the disorder's symptom heterogeneity and the limited power of available datasets. Recent long COVID GWASs have highlighted key genetic associations, such as variants close to FOXP4, ABO, and HLA-DQA1, while underscoring the limitations posed by small sample sizes, restricted diversity, and misclassification biases.

Here, an integrative analysis using COVID-19 GWAS data from the Host Genetics Initiative (HGI, release 7) was performed by leveraging proxy phenotypes to overcome the challenges of limited sample size or heterogeneity of long COVID cohorts, resulting in 62 independent single nucleotide polymorphisms (SNPs) prioritized as potentially associated with long COVID. These SNPs were categorized into three groups:

(1) severe COVID-19-specific SNPs, such as SNPs mapped to two well-known loci involved in SARS-CoV2 entry (ACE2 [rs190509934] and TMPRSS2 [rs12329760]), as well as variants of DPP9 (rs7251000), FOXP4 (rs12660421) and HLA-DQA1 (rs17219281), exhibiting associations with severe COVID-19 but displaying weaker signals in non-hospitalized COVID-19 cases;

(2) SNPs associated with both severe and mild COVID-19, including the SNP close to ABO (rs505922), representing a catalog of SNPs predispose to both acute COVID-19 and chronic long COVID;

and (3) non-hospitalization-specific SNPs, such as variants in KCTD16 (rs62401842) and WASF3 (rs56143829), highlighting genetic contributors specific to mild COVID-19 cases that might also contribute to long COVID.

Further transcriptome-wide association studies (TWASs) across 48 GTEx tissues, leveraging GWAS data on COVID-19 hospitalization, susceptibility, and long COVID from the HGI consortium, revealed distinct tissue-specific patterns of association. Compared to the acute COVID-19 phenotypes, long COVID exhibited weaker association signals across heart, brain, and muscle-related tissues, as determined by correlations between gene expression of adjacent genes of candidate SNPs (43 out of 62 SNPs) and different COVID phenotypes. Notable TWAS hits included DPP9 (rs7251000), CCR1 (rs17078348), and THBS3 (rs41264915). Phenome-wide TWASs also identified additional significant associations with long COVID related phenotypes, such as HLA-DQA1 (rs17219281), HLA-A (rs9260038), and HLA-C (rs1634761) associated with immune-related diseases, GSDMB (rs9916158) associated with asthma, FOXP4 (rs12660421) linked with sleep duration, highlighting their potential roles in long COVID pathophysiology.

Therefore, current integrative approach offers a scalable framework for long COVID research by maximizing the statistical power of existing large-scale COVID-19 GWASs and provides novel insights into the genetic underpinnings of long COVID.

Link | PDF (Preprint: MedRxiv) [Open Access]

 

Merged thread

Long COVID Risk Loci Implicated from Genome-Wide Association Studies of COVID-19 Susceptibility and Hospitalization

Abstract:
Long COVID presents a significant public health challenge, characterized by over 200 reported symptoms spanning multiple organ systems, and genome-wide association studies (GWAS) has been hindered by the disorder's symptom heterogeneity and the limited power of available datasets. To overcome the challenges of limited sample size or heterogeneity of long COVID cohorts, an integrative analysis using COVID-19 GWAS data from the Host Genetics Initiative (HGI, release 7) is performed by leveraging proxy phenotypes, resulting in 62 independent single nucleotide polymorphisms (SNPs) prioritized as potentially associated with long COVID.

These SNPs are categorized into three groups: (1) severe COVID-19-specific SNPs, such as SNPs mapped to two well-known loci involved in SARS-CoV2 entry (ACE2 [rs190509934] and TMPRSS2 [rs12329760]), as well as variants of DPP9 (rs7251000), FOXP4 (rs12660421) and HLA-DQA1 (rs17219281), exhibiting associations with severe COVID-19 but displaying weaker signals in non-hospitalized COVID-19 cases; (2) SNPs associated with both severe and mild COVID-19, including the SNP close to ABO (rs505922), representing a catalog of SNPs predispose to both acute COVID-19 and long COVID; and (3) non-hospitalization-specific SNPs, such as variants in KCTD16 (rs62401842) and WASF3 (rs56143829), highlighting genetic contributors specific to mild COVID-19 cases that might also contribute to long COVID.

Further transcriptome-wide association studies (TWASs) across 48 GTEx tissues, reveal that long COVID exhibits weaker association signals across heart, brain, and muscle-related tissues, as determined by correlations between gene expression of adjacent genes of candidate SNPs (43 out of 62 SNPs) and different COVID phenotypes, with notable TWAS hits including DPP9 (rs7251000), CCR1 (rs17078348), and THBS3 (rs41264915). Phenome-wide TWASs also link HLA-DQA1 (rs17219281), HLA-A (rs9260038), and HLA-C (rs1634761), GSDMB (rs9916158) and FOXP4 (rs12660421) with long COVID. Taken together, current integrative approach offers a scalable framework for long COVID research by maximizing the statistical power of existing large-scale COVID-19 GWASs and provides novel insights into the genetic underpinnings of long COVID.

Link (SSRN preprint, March 2025)

 

First I've heard of it, so no, we haven't considered being involved. However, we are an ME/CFS study, with our cohort being almost exclusively pre-Covid, so we might not fit with them.

 

Is it big that they found WASF3 as an implicated gene?
Is that a sort of indirect replication of the findings by Hwang’s team?

 

Do we know how many PwLC this is based on? Is 62 SNPs a lot to find?